Exploring wind data using the Meteostat Python API:
Source: Meteostat Python API
Group Members: Travis, Ira, Micah
Course: Data Science
Goal: Use ML models to predict wind trends in distinct U.S. regions
Key Variableswspd: Average wind speed (mph)wdir: Mean wind direction (degrees)coco: Condition CodeTime Period: 2024
Frame: Hourly and Daily
| Regional Wind Analysis by Speed and Direction | ||||
| Hourly Averages 2024 | Data: Meteostat | ||||
| latitude | longitude | Wind Statistics | ||
|---|---|---|---|---|
| Speed (mph) | Direction (°) | |||
| Midwest | ||||
| Cleveland, OH | 41.4993 | -81.6944 | 40.1 | 227.0 |
| Chicago, IL | 41.8781 | -87.6298 | 35.4 | 259.0 |
| Detroit, MI | 42.3314 | -83.0458 | 34.8 | 248.0 |
| Milwaukee, WI | 43.0389 | -87.9065 | 33.9 | 297.0 |
| Minneapolis, MN | 44.9778 | -93.265 | 28.2 | 307.0 |
| Northeast | ||||
| Buffalo, NY | 42.8864 | -78.8784 | 46.0 | 243.0 |
| Boston, MA | 42.3601 | -71.0589 | 38.2 | 278.0 |
| Philadelphia, PA | 39.9526 | -75.1652 | 31.0 | 297.0 |
| Pittsburgh, PA | 40.4406 | -79.9959 | 25.3 | 299.0 |
| New York, NY | 40.7128 | -74.006 | 23.8 | 300.0 |
| Southeast | ||||
| Jacksonville, FL | 30.3322 | -81.6557 | 32.5 | 81.0 |
| Miami, FL | 25.7617 | -80.1918 | 29.4 | 81.0 |
| Tampa, FL | 27.9506 | -82.4572 | 26.0 | 49.0 |
| Charlotte, NC | 35.2271 | -80.8431 | 22.7 | 319.0 |
| Atlanta, GA | 33.749 | -84.388 | 17.8 | 357.0 |
| West | ||||
| Denver, CO | 39.7392 | -104.9903 | 30.2 | 180.0 |
| San Francisco, CA | 37.7749 | -122.4194 | 29.9 | 294.0 |
| Los Angeles, CA | 34.0522 | -118.2437 | 24.6 | 208.0 |
| Portland, OR | 45.5152 | -122.6784 | 24.4 | 333.0 |
| Seattle, WA | 47.6062 | -122.3321 | 18.6 | 191.0 |
| Legend: 🔵North 🔴East 🟡South 🟢West | Darker = Stronger | ||||
R²: 0.620057846963885
RMSE: 54.64698786819519How do weather patterns change by region?
What are some case studies of extreme weather?
How do geographical features (lakes, oceans, mountains, deserts, plains) impact weather patterns?